import onnxruntime import numpy as np import cv2 import copy import os import argparse from PIL import Image, ImageDraw, ImageFont import time plate_color_list=['黑色','蓝色','绿色','白色','黄色'] plateName=r"#京沪津渝冀晋蒙辽吉黑苏浙皖闽赣鲁豫鄂湘粤桂琼川贵云藏陕甘青宁新学警港澳挂使领民航危0123456789ABCDEFGHJKLMNPQRSTUVWXYZ险品" mean_value,std_value=((0.588,0.193))#识别模型均值标准差 def decodePlate(preds): #识别后处理 pre=0 newPreds=[] for i in range(len(preds)): if preds[i]!=0 and preds[i]!=pre: newPreds.append(preds[i]) pre=preds[i] plate="" for i in newPreds: plate+=plateName[int(i)] return plate # return newPreds def rec_pre_precessing(img,size=(48,168)): #识别前处理 img =cv2.resize(img,(168,48)) img = img.astype(np.float32) img = (img/255-mean_value)/std_value #归一化 减均值 除标准差 img = img.transpose(2,0,1) #h,w,c 转为 c,h,w img = img.reshape(1,*img.shape) #channel,height,width转为batch,channel,height,channel return img def get_plate_result(img,session_rec): #识别后处理 img =rec_pre_precessing(img) y_onnx_plate,y_onnx_color = session_rec.run([session_rec.get_outputs()[0].name,session_rec.get_outputs()[1].name], {session_rec.get_inputs()[0].name: img}) index =np.argmax(y_onnx_plate,axis=-1) index_color = np.argmax(y_onnx_color) plate_color = plate_color_list[index_color] # print(y_onnx[0]) plate_no = decodePlate(index[0]) return plate_no,plate_color def allFilePath(rootPath,allFIleList): #遍历文件 fileList = os.listdir(rootPath) for temp in fileList: if os.path.isfile(os.path.join(rootPath,temp)): allFIleList.append(os.path.join(rootPath,temp)) else: allFilePath(os.path.join(rootPath,temp),allFIleList) def get_split_merge(img): #双层车牌进行分割后识别 h,w,c = img.shape img_upper = img[0:int(5/12*h),:] img_lower = img[int(1/3*h):,:] img_upper = cv2.resize(img_upper,(img_lower.shape[1],img_lower.shape[0])) new_img = np.hstack((img_upper,img_lower)) return new_img def order_points(pts): # 关键点排列 按照（左上，右上，右下，左下）的顺序排列 rect = np.zeros((4, 2), dtype = "float32") s = pts.sum(axis = 1) rect[0] = pts[np.argmin(s)] rect[2] = pts[np.argmax(s)] diff = np.diff(pts, axis = 1) rect[1] = pts[np.argmin(diff)] rect[3] = pts[np.argmax(diff)] return rect def four_point_transform(image, pts): #透视变换得到矫正后的图像，方便识别 rect = order_points(pts) (tl, tr, br, bl) = rect widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2)) widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2)) maxWidth = max(int(widthA), int(widthB)) heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2)) heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2)) maxHeight = max(int(heightA), int(heightB)) dst = np.array([ [0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], dtype = "float32") M = cv2.getPerspectiveTransform(rect, dst) warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight)) # return the warped image return warped def my_letter_box(img,size=(640,640)): # h,w,c = img.shape r = min(size[0]/h,size[1]/w) new_h,new_w = int(h*r),int(w*r) top = int((size[0]-new_h)/2) left = int((size[1]-new_w)/2) bottom = size[0]-new_h-top right = size[1]-new_w-left img_resize = cv2.resize(img,(new_w,new_h)) img = cv2.copyMakeBorder(img_resize,top,bottom,left,right,borderType=cv2.BORDER_CONSTANT,value=(114,114,114)) return img,r,left,top def xywh2xyxy(boxes): #xywh坐标变为 左上 ，右下坐标 x1,y1 x2,y2 xywh =copy.deepcopy(boxes) xywh[:,0]=boxes[:,0]-boxes[:,2]/2 xywh[:,1]=boxes[:,1]-boxes[:,3]/2 xywh[:,2]=boxes[:,0]+boxes[:,2]/2 xywh[:,3]=boxes[:,1]+boxes[:,3]/2 return xywh def my_nms(boxes,iou_thresh): #nms index = np.argsort(boxes[:,4])[::-1] keep = [] while index.size >0: i = index[0] keep.append(i) x1=np.maximum(boxes[i,0],boxes[index[1:],0]) y1=np.maximum(boxes[i,1],boxes[index[1:],1]) x2=np.minimum(boxes[i,2],boxes[index[1:],2]) y2=np.minimum(boxes[i,3],boxes[index[1:],3]) w = np.maximum(0,x2-x1) h = np.maximum(0,y2-y1) inter_area = w*h union_area = (boxes[i,2]-boxes[i,0])*(boxes[i,3]-boxes[i,1])+(boxes[index[1:],2]-boxes[index[1:],0])*(boxes[index[1:],3]-boxes[index[1:],1]) iou = inter_area/(union_area-inter_area) idx = np.where(iou<=iou_thresh)[0] index = index[idx+1] return keep def restore_box(boxes,r,left,top): #返回原图上面的坐标 boxes[:,[0,2,5,7,9,11]]-=left boxes[:,[1,3,6,8,10,12]]-=top boxes[:,[0,2,5,7,9,11]]/=r boxes[:,[1,3,6,8,10,12]]/=r return boxes def detect_pre_precessing(img,img_size): #检测前处理 img,r,left,top=my_letter_box(img,img_size) # cv2.imwrite("1.jpg",img) img =img[:,:,::-1].transpose(2,0,1).copy().astype(np.float32) img=img/255 img=img.reshape(1,*img.shape) return img,r,left,top def post_precessing(dets,r,left,top,conf_thresh=0.3,iou_thresh=0.5):#检测后处理 choice = dets[:,:,4]>conf_thresh dets=dets[choice] dets[:,13:15]*=dets[:,4:5] box = dets[:,:4] boxes = xywh2xyxy(box) score= np.max(dets[:,13:15],axis=-1,keepdims=True) index = np.argmax(dets[:,13:15],axis=-1).reshape(-1,1) output = np.concatenate((boxes,score,dets[:,5:13],index),axis=1) reserve_=my_nms(output,iou_thresh) output=output[reserve_] output = restore_box(output,r,left,top) return output def rec_plate(outputs,img0,session_rec): #识别车牌 dict_list=[] for output in outputs: result_dict={} rect=output[:4].tolist() land_marks = output[5:13].reshape(4,2) roi_img = four_point_transform(img0,land_marks) label = int(output[-1]) score = output[4] if label==1: #代表是双层车牌 roi_img = get_split_merge(roi_img) plate_no,plate_color = get_plate_result(roi_img,session_rec) result_dict['rect']=rect result_dict['landmarks']=land_marks.tolist() result_dict['plate_no']=plate_no result_dict['roi_height']=roi_img.shape[0] result_dict['plate_color']=plate_color dict_list.append(result_dict) return dict_list def cv2ImgAddText(img, text, left, top, textColor=(0, 255, 0), textSize=20): #将识别结果画在图上 if (isinstance(img, np.ndarray)): #判断是否OpenCV图片类型 img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) draw = ImageDraw.Draw(img) fontText = ImageFont.truetype( "fonts/platech.ttf", textSize, encoding="utf-8") draw.text((left, top), text, textColor, font=fontText) return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR) def draw_result(orgimg,dict_list): result_str ="" for result in dict_list: rect_area = result['rect'] x,y,w,h = rect_area[0],rect_area[1],rect_area[2]-rect_area[0],rect_area[3]-rect_area[1] padding_w = 0.05*w padding_h = 0.11*h rect_area[0]=max(0,int(x-padding_w)) rect_area[1]=min(orgimg.shape[1],int(y-padding_h)) rect_area[2]=max(0,int(rect_area[2]+padding_w)) rect_area[3]=min(orgimg.shape[0],int(rect_area[3]+padding_h)) height_area = result['roi_height'] landmarks=result['landmarks'] result = result['plate_no'] result_str+=result+" " for i in range(4): #关键点 cv2.circle(orgimg, (int(landmarks[i][0]), int(landmarks[i][1])), 5, clors[i], -1) cv2.rectangle(orgimg,(rect_area[0],rect_area[1]),(rect_area[2],rect_area[3]),(255,255,0),2) #画框 if len(result)>=1: orgimg=cv2ImgAddText(orgimg,result,rect_area[0]-height_area,rect_area[1]-height_area-10,(0,255,0),height_area) print(result_str) return orgimg if __name__ == "__main__": begin = time.time() parser = argparse.ArgumentParser() parser.add_argument('--detect_model',type=str, default=r'weights/plate_detect.onnx', help='model.pt path(s)') #检测模型 parser.add_argument('--rec_model', type=str, default='weights/plate_rec_color.onnx', help='model.pt path(s)')#识别模型 parser.add_argument('--image_path', type=str, default='imgs', help='source') parser.add_argument('--img_size', type=int, default=640, help='inference size (pixels)') parser.add_argument('--output', type=str, default='result1', help='source') opt = parser.parse_args() file_list = [] allFilePath(opt.image_path,file_list) providers = ['CPUExecutionProvider'] clors = [(255,0,0),(0,255,0),(0,0,255),(255,255,0),(0,255,255)] img_size = (opt.img_size,opt.img_size) session_detect = onnxruntime.InferenceSession(opt.detect_model, providers=providers ) session_rec = onnxruntime.InferenceSession(opt.rec_model, providers=providers ) if not os.path.exists(opt.output): os.mkdir(opt.output) save_path = opt.output count = 0 for pic_ in file_list: count+=1 print(count,pic_,end=" ") img=cv2.imread(pic_) img0 = copy.deepcopy(img) img,r,left,top = detect_pre_precessing(img,img_size) #检测前处理 # print(img.shape) y_onnx = session_detect.run([session_detect.get_outputs()[0].name], {session_detect.get_inputs()[0].name: img})[0] outputs = post_precessing(y_onnx,r,left,top) #检测后处理 result_list=rec_plate(outputs,img0,session_rec) ori_img = draw_result(img0,result_list) img_name = os.path.basename(pic_) save_img_path = os.path.join(save_path,img_name) cv2.imwrite(save_img_path,ori_img) print(f"总共耗时{time.time()-begin} s")